Uncertainty estimation for a new exponential-filter-based long-term root-zone soil moisture dataset from Copernicus Climate Change Service (C3S) surface observations

Pasik, Adam; Gruber, Alexander; Preimesberger, Wolfgang; De Santis, Domenico; Dorigo, Wouter

doi:https://doi.org/10.5194/gmd-16-4957-2023

Articles | Volume 16, issue 17

https://doi.org/10.5194/gmd-16-4957-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-16-4957-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 16, issue 17

Model evaluation paper

|

31 Aug 2023

Model evaluation paper |

| 31 Aug 2023

Uncertainty estimation for a new exponential-filter-based long-term root-zone soil moisture dataset from Copernicus Climate Change Service (C3S) surface observations

Adam Pasik, Alexander Gruber, Wolfgang Preimesberger, Domenico De Santis, and Wouter Dorigo

Data sets

2002-2020 Error-characterized Root-zone Soil Moisture (0-2 m) from C3S Surface Observations Adam Pasik and Wolfgang Preimesberger https://doi.org/10.48436/9gsg6-nn854

Model code and software

TUW-GEO/pyswi: v1.0 (v1.0) Adam Pasik, Bernhard Gößwein, Sebastian Hahn, Wolfgang Preimesberger, and Bernhard Bauer-Marschallinger https://doi.org/10.5281/zenodo.7534919

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Short summary

We apply the exponential filter (EF) method to satellite soil moisture retrievals to estimate the water content in the unobserved root zone globally from 2002–2020. Quality assessment against an independent dataset shows satisfactory results. Error characterization is carried out using the standard uncertainty propagation law and empirically estimated values of EF model structural uncertainty and parameter uncertainty. This is followed by analysis of temporal uncertainty variations.